Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter
Develops a natural language processing approach to distinguish automated bot accounts from organic human authors on Twitter, enabling more reliable social media health research.
This publication published in Journal of Computational Science represents peer-reviewed research in Digital Health / Social Media directly relevant to Aimwell’s evidence intelligence infrastructure. It contributes to the FHIN network’s knowledge base on Digital Health / Social Media and supports data-driven clinical decision making for Aimwell member organizations.
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